Solar energetic particle time series analysis with Python

dc.contributor.authorPalmroos Christian
dc.contributor.authorGieseler Jan
dc.contributor.authorDresing Nina
dc.contributor.authorMorosan Diana E.
dc.contributor.authorAsvestari Eleanna
dc.contributor.authorYli-Laurila Aleksi
dc.contributor.authorPrice Daniel J.
dc.contributor.authorValkila Saku
dc.contributor.authorVainio Rami
dc.contributor.organizationfi=avaruustutkimuslaboratorio|en=Space Research Laboratory|
dc.contributor.organization-code1.2.246.10.2458963.20.47833719389
dc.contributor.organization-code2606702
dc.converis.publication-id177352110
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/177352110
dc.date.accessioned2022-12-21T03:30:28Z
dc.date.available2022-12-21T03:30:28Z
dc.description.abstract<p>Solar Energetic Particles (SEPs) are charged particles accelerated within the solar atmosphere or the interplanetary space by explosive phenomena such as solar flares or Coronal Mass Ejections (CMEs). Once injected into the interplanetary space, they can propagate towards Earth, causing space weather related phenomena. For their analysis, interplanetary <em>in situ</em> measurements of charged particles are key. The recently expanded spacecraft fleet in the heliosphere not only provides much-needed additional vantage points, but also increases the variety of missions and instruments for which data loading and processing tools are needed. This manuscript introduces a series of Python functions that will enable the scientific community to download, load, and visualize charged particle measurements of the current space missions that are especially relevant to particle research as time series or dynamic spectra. In addition, further analytical functionality is provided that allows the determination of SEP onset times as well as their inferred injection times. The full workflow, which is intended to be run within Jupyter Notebooks and can also be approachable for Python laymen, will be presented with scientific examples. All functions are written in Python, with the source code publicly available at GitHub under a permissive license. Where appropriate, available Python libraries are used, and their application is described.</p>
dc.identifier.eissn2296-987X
dc.identifier.jour-issn2296-987X
dc.identifier.olddbid190720
dc.identifier.oldhandle10024/173811
dc.identifier.urihttps://www.utupub.fi/handle/11111/30728
dc.identifier.urlhttps://doi.org/10.3389/fspas.2022.1073578
dc.identifier.urnURN:NBN:fi-fe2022122172994
dc.language.isoen
dc.okm.affiliatedauthorPalmroos, Christian
dc.okm.affiliatedauthorGieseler, Jan
dc.okm.affiliatedauthorGieseler, Nina
dc.okm.affiliatedauthorYli-Laurila, Aleksi
dc.okm.affiliatedauthorValkila, Saku
dc.okm.affiliatedauthorVainio, Rami
dc.okm.discipline115 Astronomy and space scienceen_GB
dc.okm.discipline115 Avaruustieteet ja tähtitiedefi_FI
dc.okm.internationalcopublicationnot an international co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA1 ScientificArticle
dc.publisherFrontiers Media S.A.
dc.publisher.countrySwitzerlanden_GB
dc.publisher.countrySveitsifi_FI
dc.publisher.country-codeCH
dc.relation.doi10.3389/fspas.2022.1073578
dc.relation.ispartofjournalFrontiers in Astronomy and Space Sciences
dc.source.identifierhttps://www.utupub.fi/handle/10024/173811
dc.titleSolar energetic particle time series analysis with Python
dc.year.issued2022

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